Kamyar Nazeri

@ljjcoder If you already have downloaded the high-resolution Places2 dataset, you can set `INPUT_SIZE: 256` in your configuration file and change the following line in the code and pass `centerCrop=False`...

@ljjcoder You don't need to change the method definition. Only change the method call. Of course that is if you have already downloaded the high-resolution version of the Places2 dataset....

@ljjcoder Honestly it doesn't really make any difference. You can either center crop an image or resize it to a fixed size. In either of these scenarios, the mask hides...

@ljjcoder This is the right script to test FID. However, since it's using Inception model, make sure that you run the script on GPU using `--gpu` flag: ````bash python ./scripts/fid_score.py...

@ljjcoder The inception model is only used to extract deep features from input images. The Frechet distance measures the [distance between two multivariate normals](https://www.sciencedirect.com/science/article/pii/0047259X8290077X), that means the input distribution has...

@ljjcoder First off, 1,000 images are not enough to capture an entire distribution. I've seen in papers that the FID is reported for more than 10,000 (sometimes 25,000) images! Second,...

I have no idea why the training stops, I think you can debug the code here: This is where we save the models after `SAVE_INTERVAL` intervals. https://github.com/knazeri/edge-connect/blob/97c28c62ac54a59212cc9db4e78f36c5436c0b72/src/edge_connect.py#L205-L206 As for your...

@wangping1408 You don't need to do any pre-processing step! All pre-processing steps, including resizing to the proper size, is implemented in our code! The edge_flist that you mentioned is for...

@wangping1408 That's exactly how you specify muli-gpu! Can you make sure that you can train separately on `GPU: [0]` and then `GPU: [1]`? Because the error you are receiving is...

Can you print the value of `config.GPU` before this line? https://github.com/knazeri/edge-connect/blob/698509d1ac1d7a40310139f9e4d70410b3d734e4/src/models.py#L67 Because the error is thrown by the internal `torch.cuda.get_device_properties` method, which only throws an error with invalid GPU id!